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Vijeta Sharma, Manjari Gupta, Ajai Kumar and Deepti Mishra
The video camera is essential for reliable activity monitoring, and a robust analysis helps in efficient interpretation. The systematic assessment of classroom activity through videos can help understand engagement levels from the perspective of both stu...
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Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o...
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Olena Pavliuk, Myroslav Mishchuk and Christine Strauss
Over the last few years, human activity recognition (HAR) has drawn increasing interest from the scientific community. This attention is mainly attributable to the proliferation of wearable sensors and the expanding role of HAR in such fields as healthca...
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Yaxin Mao, Lamei Yan, Hongyu Guo, Yujie Hong, Xiaocheng Huang and Youwei Yuan
Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. I...
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Abdorreza Alavigharahbagh, Vahid Hajihashemi, José J. M. Machado and João Manuel R. S. Tavares
In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and de...
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Hossein Shahverdi, Mohammad Nabati, Parisa Fard Moshiri, Reza Asvadi and Seyed Ali Ghorashi
Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human?Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representa...
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Yubo Zheng, Yingying Luo, Hengyi Shao, Lin Zhang and Lei Li
Contrastive learning, as an unsupervised technique, has emerged as a prominent method in time series representation learning tasks, serving as a viable solution to the scarcity of annotated data. However, the application of data augmentation methods duri...
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Baha A. Alsaify, Mahmoud M. Almazari, Rami Alazrai, Sahel Alouneh and Mohammad I. Daoud
Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. One of the recently used techniques showing tremendous potential is channel state information (C...
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Oscar Herrera-Alcántara
In this paper, fractional calculus principles are considered to implement fractional derivative gradient optimizers for the Tensorflow backend. The performance of these fractional derivative optimizers is compared with that of other well-known ones. Our ...
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Hojun Jin, Sarvar Hussain Nengroo, Inhwan Kim and Dongsoo Har
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